Client Profile

A mid-size US-based automotive equipment manufacturer serving thousands of service centers across North America. Their clientele includes car dealerships, auto repair shops, and fleet service providers. With tens of thousands of orders annually, their existing support system was overwhelmed by increasing volumes of inquiries, including product setup and troubleshooting, Order status tracking, Warranty claims etc.

The company initially relied on a rule-based chatbot that failed to understand user intent and lacked seamless query redirection, resulting in a heavy load on support agents.

Support Landscape & Challenges

High Support Team Costs (~$ 0.9 M/year)

A customer support team working in shifts led to high labor expenses without improved efficiency.

Rule-Based Chatbot Limitations

The previous chatbot could handle only a basic set of tasks (order status, FAQs) and struggled with more complex queries.

Inability to Understand Intent

The old system often failed to understand user intent, leading to frustrated customers and frequent handoffs to agents.

Scattered Documentation

Manuals and guides were available in PDFs, but were not easily searchable or organized for quick access.

Lack of query prioritization

No intelligent triaging or escalation by department or sentiment.

Order Status Inquiries

Frequent inquiries about order tracking added to the support burden.

Escalation Challenges

The lack of a structured workflow led to delays in query escalation, especially for complex issues.

No data-driven feedback tracking

No mechanisms to identify repeating issues or performance patterns.

The Cost of Inaction

The Hidden Cost of Not using AI

Annual support spend : $0.9 M
Monthly inbound contacts : 5100
Flow-bot self-service : 15 %
False escalations : 50 % of handed-off chats
Troubleshooting handle time : 15 m
CSAT (weighted) : 82 / 100

The Solution: AI-Powered Support Copilot + Intelligent Agent Ecosystem

The company initially deployed rule-based chatbots to automate order tracking and shipment updates. While this reduced basic inquiries early on, the system quickly became inadequate as customer interactions grew more complex.

Rigid flows couldn’t interpret varied customer phrasing, resulting in frequent “I didn’t understand” errors.
Complex queries still needed human intervention, creating backlogs.

To address these challenges and reduce agent workload, leadership initiated a strategic shift toward a more advanced AI-driven solution.

They partnered with our team to build a sophisticated GenAI-powered chatbot capable of managing both routine and nuanced customer queries. Seamlessly integrated with HubSpot CRM for automated ticketing and smart routing, the system was rolled out in stages - first as an internal support copilot for agents, then as a public-facing chatbot for customers.

Implementation Process

Phase 1: Discovery & Planning (1 month)

Objective : Understand the existing system's limitations and gather requirements from all stakeholders (support agents, managers, IT, and product teams).

Actions :

Stakeholder Interviews : Understand pain points and expectations.
Data Audit : Assess existing knowledge base, CRM integration needs, and workflows.
AI System Planning : Define AI capabilities, including intent recognition, ticket creation, lead qualification, and sentiment analysis.

Phase 2: System Design & Architecture (1.5 months)

Objective : Design the architecture for AI chatbot integration with HubSpot CRM, knowledge base, and other internal systems.

Actions :

Integration Mapping : Plan seamless integration between the chatbot, CRM, and order management systems.
Data Structuring : Organize existing manuals, FAQs, and product documentation into structured, vectorized formats for AI access.
AI Model Selection : Choose the appropriate models as GPT-4 for conversational AI and additional tools like Pinecone for fast document retrieval.

Phase 3: Development & AI Training (2.5 months)

Objective : Build and train the AI system based on conversational datasets of last 12 months to handle a wide range of customer queries.

Actions :

Rule-Based & NLP Hybrid Model : Develop a hybrid system that includes rule-based flows for simple queries using Langchain and an Open AI GPT 4 API for more complex, free-form inquiries.
Sentiment Analysis & Lead Qualification : Implement algorithms to analyze customer sentiment and qualify leads for with respect to potential customer.
Ticket Routing Logic : Integrate AI capabilities to create a ticket and automatically route based on department, agent experience, and customer sentiment.
Initial Training : Train the AI on historical customer queries, order information, and troubleshooting flows.

Phase 4: Internal Copilot Deployment & Testing (2 months)

Objective : Deploy the chatbot internally as a "copilot" to assist live support agents and validate AI performance.

Actions :

Testing : Ensure the chatbot can handle at least 40% of customer queries, leaving complex cases to be escalated to agents.
Feedback Loop : Collect real-time feedback from support agents to improve the AI's understanding and responses.
Failure Logging : Unanswered or misrouted queries are logged for weekly analysis and continuous training of the AI model.
KPIs Monitoring : Track response times, customer satisfaction (CSAT), and lead conversion rates to gauge chatbot success.

Phase 5: Public Rollout (1 month)

Objective : Roll out the chatbot to all customers and fully integrate with the HubSpot CRM for ticket management.

Actions :

Frontend Integration : Embed the chatbot widget on the website, ensuring a seamless user experience.
API Connections : Finalize integration with HubSpot CRM to automate ticket creation and track customer interactions.
Post-Launch Support : Provide continuous monitoring and optimization based on real-world usage.
User Notification : End-users are clearly informed when they are being redirected to an agent, preserving transparency and trust.

Core Capabilities Delivered

Intent Recognition & Ticket Creation (via GPT + Langchain + HubSpot API)
Order Tracking Integration (via secure internal APIs)
Product Recommendations & Cross-Sell Logic
Real-time Sentiment & Feedback Analysis
Smart Escalation Based on Context, Tone, and Agent Specialization
Multi-turn Conversations with Context Memory
KPI Monitoring Dashboards for Ops Teams

Security & Governance Highlights

Guard-rails via LangChain

Safety keyword filter + confidence gate (≤ 0.8) ➜ auto-escalate, add disclaimer.

Auditability

Git-tagged prompt/chain snapshots stored in S3 (30-day retention) with blue-green alias for one-click rollback

Cost control

Token/second alarms; most-asked answers cached in deterministic chains to bypass GPT.

Technology Stack

OpenAI GPT-4 API

For natural language understanding and generation.

Pinecone

Fast vector store for document retrieval and knowledge base search.

HubSpot CRM

Integrated for ticket management and customer relationship tracking.

Langchain

Orchestration and chaining for AI actions and API calls.

React.js

For embedding the chatbot widget with real-time interactions.

AWS (Docker)

For hosting the solution in a secure, scalable cloud environment.

Python

For backend service development, including sentiment analysis, ticket routing, and API integration.

Results

KPI	Before	After	Impact
Self-served contacts	15 %	55 %	+≈2 800 issues/mo auto-resolved
False escalations	50 %	11 %	78 % Reduction
Avg troubleshooting time	15 min	6 min 20 s	58 % Reduction
Tier-1 FTE	10	7	3 agents redeployed to proactive upsell queue
Annual support spend	$0.90 M	$0.65 M	$245 k hard saving
CSAT	82	91	+9 pts
Cross-/Upsell conversion rate	0.8 %	3.1 %	+2.3 pp
Net incremental revenue from AI-driven offers	-	$1.25 M / yr	New top-line lift

Customer Feedback

The AI is a game-changer. It helps us get answers instantly without waiting for an agent, and the escalation to human agents is smooth.

Regional Service Manager

I tracked my order and got answers to my questions in under a minute, faster than I’ve ever experienced.

Independent Garage Owner

Post-Deployment Optimization

Post-launch, the team continues to review feedback and optimize the AI model based on real-world interactions. The system is continuously retrained with new data, ensuring it remains accurate and efficient. The integration with the CRM has streamlined ticket management, and the AI’s ability to track sentiment and qualify leads has opened up new upsell and cross-sell opportunities.

Team Composition

Project Manager: 1
AI / ML Engineers: 2
Backend Devs: 2
Front-end Dev: 1
CRM Specialist: 1
QA / Sec-QA: 1
Content Ops: 3

Testimonials

Our Slam Book

Tony Lehtimaki

DIRECTOR - AMEOS

Spain

Very professional, accurate and efficient team despite all the changes I had them do. I look forward to working with them again.

Antoine de Bausset

CEO - BEESPOKE

France

They are great at what they do. Very easy to communicate with and they came through faster than I hoped. They delivered everything I wanted and more! I will certainly use them again!

Vivek Singh

MARKETING & SALES HEAD - VARMORA

Gujarat

I really liked their attention to detail and their sheer will to do the job at hand as good as possible beyond professional boundaries.

Nimesh Patel

DIRECTOR - COVERTEK CERAMICA

Gujarat

Excellent work, and on time with all goals. Communication was very easy, and knowledge of work was excellent. Will be working with them on upcoming projects. I highly recommend.

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DIRECTOR - ENA PARAMUS

United States

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CO-FOUNDER - ESR

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